BMC Genomics (Mar 2022)

Identification and validation of a prognostic model for melanoma patients with 9 ferroptosis-related gene signature

  • Yuxuan Chen,
  • Linlin Guo,
  • Zijie Zhou,
  • Ran An,
  • Jiecong Wang

DOI
https://doi.org/10.1186/s12864-022-08475-y
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 15

Abstract

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Abstract Background Melanoma is a highly heterogeneous and aggressive cutaneous malignancy. Ferroptosis, a new pathway of cell death depending on the intracellar iron, has been shown to be significantly associated with apoptosis of a number of tumors, including melanoma. Nevertheless, the relationship between ferroptosis-related genes (FRGs) and the melanoma patients’ prognosis needs to be explored. Methods Download expression profiles of FRGs and clinical data from The Cancer Genome Atlas (TCGA) database. 70% data were randomly selected from the TCGA database and utilized the univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) regression model to create a prognostic model, and the remaining 30% was used to validate the predictive power of the model. In addition, GSE65904 and GSE22153 date sets as the verification cohort to testify the predictive ability of the signature. Results We identified nine FRGs relating with melanoma patients’ overall survival (OS) and established a prognostic model based on their expression. During the research, patients were divided into group of high-risk and low-risk according to the results of LASSO regression analysis. Survival time was significantly longer in the low-risk group than that of in the high-risk group (P < 0.001). Enrichment analysis of different risk groups demonstrated that the reasons for the difference were related to immune-related pathways, and the degree of immune cell infiltration in the low-risk group was significantly higher than that in the high-risk group. Conclusions The FRG prognostic model we established can predict the prognosis of melanoma patients and may further guide subsequent treatment.

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